Meta-Activity Recognition:A Wearable Approach for Logic Cognition-based Activity Sensing Lei Xie,Xu Dong,Wei Wang,and Dawei Huang State Key Laboratory for Novel Software Technology,Nanjing University,China Email:Ixie@nju.edu.cn,dongxu@dislab.nju.edu.cn,ww@nju.edu.cn,huangdw @dislab.nju.edu.cn Abstract-Activity sensing has become a key technology for activity.Therefore,traditional activity sensing schemes [1] many ubiquitous applications,such as exercise monitoring and 3]are either based on the user-dependent recognition,which elder care.Most traditional approaches track the human motions requires to record the training data from the current user and perform activity recognition based on the waveform match- ing schemes in the raw data representation level.In regard to to improve the recognition accuracy,or relying on heavy- the complex activities with relatively large moving range,they training,which requires to collect a large quantity of training usually fail to accurately recognize these activities,due to the samples to build the templates.It is essential to propose inherent variations in human activities.In this paper,we propose a brand-new activity sensing scheme,such that the derived a wearable approach for logic cognition-based activity sensing recognition models can be scalable to any arbitrary human scheme in the logical representation level,by leveraging the meta- activity recognition.Our solution extracts the angle profiles from subjects in a user-independent and light-training approach. the raw inertial measurements,to depict the angle variation In this paper,we propose a wearable approach for logic cog- of limb movement in regard to the consistent body coordinate nition-based activity sensing,by leveraging the meta-activity system.It further extracts the meta-activity profiles to depict the recognition in the logical representation level.We mainly focus sequence of small-range activity units in the complex activity By leveraging the least edit distance-based matching scheme,our on the complex activities from human subjects,as shown in solution is able to accurately perform the activity sensing.Based Fig.1.Our approach is based on the observation that when on the logic cognition-based activity sensing,our solution achieves the human subject is performing an arbitrary activity,he/she lightweight-training recognition,which requires a small quantity is experiencing a very similar sequence of small-range-activity of training samples to build the templates,and user-independent units in the logical aspect,despite of the detailed differences in recognition,which requires no training from the specific user. The experiment results in real settings shows that our meta- the waveforms of the raw inertial measurements.We leverage activity recognition achieves an average accuracy of 92%for the notion meta-activity to denote the small-range-activity user-independent activity sensing units which compose a common activity of human subject. Given an arbitrary activity,our approach first extracts the I.INTRODUCTION angle profiles from the raw measurements to depict the angle Nowadays activity sensing has become a key technology for variation of limb movement in the consistent body coordinate many ubiquitous applications such as exercise monitoring and system.Then,it further extracts the meta-activity profiles to elder care.For example,in the daily exercise monitoring,it depict the sequence of small-range-activity units in the specific is essential to figure out what kinds of exercises the human activity.By leveraging the least edit distance-based matching subjects did everyday.The rising of the wearable devices has scheme,our solution is able to accurately perform the activity provided new opportunities for activity sensing during human sensing.Since a scalable recognition model is derived from motion.The wearable devices such as the smart watches are the meta-activity-based templates in the logical representation usually embedded with inertial sensors like the accelerometers, level,our solution achieves lightweight-training recognition, gyroscopes and magnetometers.They are able to continuously which requires a small quantity of training samples to build the track the human subject's movements and classify them into templates,and user-independent recognition,which requires the corresponding activities by matching the waveforms of in- no training from the specific user. ertial measurements against the templates.However,a number of common activities,e.g.,dumbbell curl and rope skipping, belong to the complex activities.The complex activity refers to an activity which has large range of movement and incurs rotations on multiple joints of the limbs,e.g.,the movement 1:Upright 2:Dumbbell 3:Dumbbell 4:Dumbbell 5:Dumbbell has angle change of more than 45 and involves more than arbell Rov ●urH Flies .随tern长速st Triceps Extensio 2 joints of the limbs.Moreover,it usually has two complex aspects:the widespread variations in activity details and the large movement range.Due to the user-specific characters like the heights,limb lengths and moving behaviors,there Rope Skipping 9:Pin 0:Ba exist obvious deviations in the raw inertial measurements from Swing different human subjects during the process of the complex Fig.1.Example Complex ActivitiesMeta-Activity Recognition: A Wearable Approach for Logic Cognition-based Activity Sensing Lei Xie, Xu Dong, Wei Wang, and Dawei Huang State Key Laboratory for Novel Software Technology, Nanjing University, China Email: lxie@nju.edu.cn, dongxu@dislab.nju.edu.cn, ww@nju.edu.cn, huangdw@dislab.nju.edu.cn Abstract—Activity sensing has become a key technology for many ubiquitous applications, such as exercise monitoring and elder care. Most traditional approaches track the human motions and perform activity recognition based on the waveform match￾ing schemes in the raw data representation level. In regard to the complex activities with relatively large moving range, they usually fail to accurately recognize these activities, due to the inherent variations in human activities. In this paper, we propose a wearable approach for logic cognition-based activity sensing scheme in the logical representation level, by leveraging the meta￾activity recognition. Our solution extracts the angle profiles from the raw inertial measurements, to depict the angle variation of limb movement in regard to the consistent body coordinate system. It further extracts the meta-activity profiles to depict the sequence of small-range activity units in the complex activity. By leveraging the least edit distance-based matching scheme, our solution is able to accurately perform the activity sensing. Based on the logic cognition-based activity sensing, our solution achieves lightweight-training recognition, which requires a small quantity of training samples to build the templates, and user-independent recognition, which requires no training from the specific user. The experiment results in real settings shows that our meta￾activity recognition achieves an average accuracy of 92% for user-independent activity sensing. I. INTRODUCTION Nowadays activity sensing has become a key technology for many ubiquitous applications such as exercise monitoring and elder care. For example, in the daily exercise monitoring, it is essential to figure out what kinds of exercises the human subjects did everyday. The rising of the wearable devices has provided new opportunities for activity sensing during human motion. The wearable devices such as the smart watches are usually embedded with inertial sensors like the accelerometers, gyroscopes and magnetometers. They are able to continuously track the human subject’s movements and classify them into the corresponding activities by matching the waveforms of in￾ertial measurements against the templates. However, a number of common activities, e.g., dumbbell curl and rope skipping, belong to the complex activities. The complex activity refers to an activity which has large range of movement and incurs rotations on multiple joints of the limbs, e.g., the movement has angle change of more than 45◦ and involves more than 2 joints of the limbs. Moreover, it usually has two complex aspects: the widespread variations in activity details and the large movement range. Due to the user-specific characters like the heights, limb lengths and moving behaviors, there exist obvious deviations in the raw inertial measurements from different human subjects during the process of the complex activity. Therefore, traditional activity sensing schemes [1]– [3] are either based on the user-dependent recognition, which requires to record the training data from the current user to improve the recognition accuracy, or relying on heavy￾training, which requires to collect a large quantity of training samples to build the templates. It is essential to propose a brand-new activity sensing scheme, such that the derived recognition models can be scalable to any arbitrary human subjects in a user-independent and light-training approach. In this paper, we propose a wearable approach for logic cog￾nition-based activity sensing, by leveraging the meta-activity recognition in the logical representation level. We mainly focus on the complex activities from human subjects, as shown in Fig. 1. Our approach is based on the observation that when the human subject is performing an arbitrary activity, he/she is experiencing a very similar sequence of small-range-activity units in the logical aspect, despite of the detailed differences in the waveforms of the raw inertial measurements. We leverage the notion meta-activity to denote the small-range-activity units which compose a common activity of human subject. Given an arbitrary activity, our approach first extracts the angle profiles from the raw measurements to depict the angle variation of limb movement in the consistent body coordinate system. Then, it further extracts the meta-activity profiles to depict the sequence of small-range-activity units in the specific activity. By leveraging the least edit distance-based matching scheme, our solution is able to accurately perform the activity sensing. Since a scalable recognition model is derived from the meta-activity-based templates in the logical representation level, our solution achieves lightweight-training recognition, which requires a small quantity of training samples to build the templates, and user-independent recognition, which requires no training from the specific user. 1:Upright Barbell Row 2:Dumbbell Curl 3:Dumbbell Flies 4:Dumbbell Lateral Raise 6: Rope Skipping 7: Butterfly 8: Cable Crossover 9: Ping-Pong Swing 5:Dumbbell Triceps Extension 10: Badminton Swing Fig. 1. Example Complex Activities